No Renal Toxicity After Repeated Treatment with Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC) in Patients with Unresectable Peritoneal Metastasis
Bibliographic record
Abstract
Background/Aim: Pressurized intraperitoneal aerosol chemotherapy (PIPAC) is a recent approach for intraperitoneal chemotherapy with promising results for patients with peritoneal metastasis (PM). The aim of this study was to report renal toxicity for patients who received at least 3 repeated PIPAC procedures. Patients and Methods: All patients who underwent at least 3 PIPAC cycles of cisplatin (7.5 mg/m2) and doxorubicin (1.5 mg/m2) for unresectable PM from December 2015 to September 2017, were analysed regarding postoperative renal toxicity. Results: Among 103 patients registered in a prospective single center database, 43 patients underwent at least 3 PIPAC cycles representing a total of 175 PIPAC. Median age was 59.8 years, 24 (55.8%) patients were female and median BMI was 22.2 kg/m2. Most common origins of PM were gastric 22 (51.1%) and ovarian 11 (25.6%) cancer. Median peritoneal cancer index (PCI) was 17 (range=5-39). For 39 (90.1%) patients, systemic chemotherapy was performed in addition to PIPAC. Forty-three (100%), 17 (39.5%), 14 (32.5%), 8 (18.6%), 3 (7%), 2 (4.7%) and 2 (4.7%) patients underwent three, four, five, six, seven, eight and nine PIPAC procedures, respectively. Repeated PIPAC did not induce significant acute nor cumulative renal toxicity in any patients. Conclusion: Repeated PIPAC did not induce clinically relevant renal toxicity. This study confirms the previous published results in a larger group of patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".